Jack Goodman with Prof Judyth Sachs

Speaker A: This is Reimagining Higher Education your go to podcast with remarkable education leaders sharing personal stories from their experience in and around the sector, including Reflections and Hope for Progress in Education with your host, Professor Judith Sachs, former PVC Learning and Teaching at the University of Sydney, Deputy Vice Chancellor and Provost at Macquarie University, Special Advisor in Higher Education at KPMG and now Chief Academic Officer at Studiosity. Welcome.

Speaker B: Good morning. I'm Judith Sachs and I'm Chief Academic Officer for Studiosity and I'm talking to Jack Goodman again as part of our, uh, Reimagining the future of higher Education. Jack, I didn't realize that the last one we did was on 16th December 2022. In our last podcast we said, oh, we should do these every six months. But sadly life got ahead of us and here we are three and a half years later. So would you like to give a catch up about what has happened both in terms of the company and what's happened in terms of the broader, uh, context of higher education in Australia, but things that you're seeing elsewhere, and then we'll talk about a number of themes that we've both identified and have a conversation as we did last time.

Speaker C: Well, thank you Judith and it is lovely to be back on the podcast with you again. Uh, I do feel like we were in a time warp or something and we lost track of the time. Clearly something momentous happened, uh, around the time of that podcast and going back and listening to it, as I know you did as well as I, uh, it's interesting that the concept of artificial intelligence was not mentioned once in that discussion. We were very concerned about all sorts of other issues, but not artificial intelligence. And I would say that that is the thing that has happened in the last three and a half years that has transformed the sector and transformed our organization and how we support universities in that context.

Speaker B: Can you elaborate a bit what Studiosity have done with this opportunity? Because I see the current period of time as, uh, an inflection point, both in terms of how universities are positioned within the communities, but also how students are positioned within universities.

Speaker C: Yeah, sure. It's been probably one of the most challenging times for everyone involved in higher education. Faculty leaders, students, administrators, uh, and certainly technology providers like ourselves. It has been absolutely astonishing. I recall the first time I used to, um, chatgpt, which would have been a week or two prior to that conversation with you and thinking then, wow, this is going to be unbelievably transformative for teaching and learning. What's that going to look like? And then I remember spending the entire summer holidays thinking about it and playing with it and being increasingly kind of amazed and astonished and also deeply concerned. And it didn't take us long to realize what it took students, even maybe less time, which was actually this thing can do, can do my work pretty well. And compared to how it works now, it was not doing work very well. It was probably a C plus student. It's now a very, very capable postgraduate PhD. You know, it's a brilliant, it's a brilliant, um, creator of university output for students. And that is both remarkable and remarkably challenging. So from, from our perspective, when we saw this technology emerge, we realized almost immediately that it had profound consequences for what we do. And we had been developing and were really global experts in providing feedback on student writing with human experts, trained professional, quality feedback delivered within 24 hours, usually maybe within 10 or 12 hours, sometimes less. But this was human feedback. And we realized that we needed to, uh, pursue the idea of using, um, large language models to have the capacity to deliver as good, if not better feedback in not hours, but one or two minutes. And that is something we managed to build and deliver by the start of 2024. And that in itself has been transformative for all of our university partners and the UH sector more broadly. That kind of real time feedback has meant students have far greater opportunities to improve their learning by going through that iterative process of learning how to think. So that's one way.

Speaker B: So when you think about the process of. At first it was massive resistance. It was, the world is, you know, it was Chicken Little, the sky's falling, uh, universities will be in decline, Quality and standards will go, you know, it's going to be terrible. Then after some reflection, it became acceptance and incorporation. But that took some time for the acceptance. Yes, we can use it. There's no point fighting it. The police and prosecution, uh, just was such a negative way to respond to a major transformational change. And now it's sort of being integrated into various learning systems with clear guardrails. But still a lot of debate. It seems to me that part of the problem is the technology is driving the change rather than rethinking learning and how technology can support and validate learning and in fact be able to, um, improve some of the processes and get deeper learning, but also better outcomes for students. What's your view of the whole process?

Speaker C: It's a really interesting way you frame that. Yeah, I agree. There was this moral panic we talk about at the, you know, in 2023, people thought, oh my Goodness, what, what does this mean? What is the future of higher education? And it was very clear even then that the technology was not only not going away but was certainly just going to continue to improve, uh, and improve at an increasingly rapid rate and that it was amazingly capable. And so we were going to have to figure out a way to incorporate it. Um, but what that looks like is actually still up for debate. And part of the reason that I think the sector and when I say that that's you know, globally, across all the geographies where we work in North America, Europe, Australia, Southeast Asia and the Pacific, the vast majority of institutions are faculty led. And the autonomy of within the academy has meant it's been very hard to come up with unified approaches that try to develop sensible education first teaching and learning first approaches to solving this problem. And put simply, professors aren't used to that kind of top down management and so they either want to develop their own or may not always be so receptive to what they hear coming from the administration. And that has left most universities highly exposed to an external narrative which is coming from the large foundation model providers. Now we all know the names of these companies, right? Um, OpenAI and anthropic and Google, et cetera, um, and that's where um, with the lack of any coordinated or cohesive response from, from higher education you have a very um, kind of aggressive narrative that universities need to be producing AI literate graduates. Whatever AI literate means, they're under enormous pressure to produce these graduates and, and employers are saying well if your graduates aren't able to use artificial intelligence, they'll be of no use in the modern world as well. So it's, we're still in actually I would argue very much of a fraught moment of sorting out what the future should and will look like. And that is quite remarkable given that we are coming up to four years into this uh, revolution, what I'm hearing

Speaker B: you say, and correct me if I'm wrong, there's been a tactical reactive response rather than a thoughtful strategic response. And I just wonder if we have to ask different sorts of questions about my ethnographic question. What's happening here? What's really happening here and what does it mean? So what are the strategic questions you think we should ask?

Speaker C: So I've been thinking a lot about this and I think one of the things that generative artificial intelligence, when it's made openly available and freely available to students, to the public, to the wide world as it has been, um, has had a powerful impact in diminishing trust in relationships. And by trust I mean an understanding of who's thinking what and who owns what ideas. Prior to these large language models, um, existence in the public release, um, in general, it was reasonably clear when a student communicated with a professor, ah, that they were producing something somewhat related to what was in their own head in terms of their understanding of an idea. Sure there were academic integrity issues, but they were understood and largely, um, they were manageable. They were becoming less manageable during COVID when all that learning moved online. But they were understood and manageable. But the AI revolution has really undermined trust in that teaching and learning relationship. And I think that is where we need a strategic reset and a rethink of how do we put trust back in as opposed to thinking about this, um, you know, this policing and punishing model which is clearly broken now that, you know, as I've, as I've tried to say many times, um, you know, one of the ways to think about generative AI is imagine, imagine it as almost a, um, an invisible cloak that a child could wear to walk into a lolly shop. And if you gave this amazing technology to a student, to a child, what are the chances that they might go and take some lollies from the lollipop? 100% right. And in many ways, um, that is what we have seen with unconstrained foundation models. The technology to break the rules of traditional rules of academic integrity suddenly become, has made this opportunity, um, ubiquitous, ostensibly free, and some would argue, undetectable. And when you have those three factors, you have the breakdown of trust in the relationship. And that's where I think we can think strategically. And this is really what studiosity has been thinking about for the last, um, you know, year, year and a half. How do we help put trust back into that teaching and learning relationship? Because without it, there really is a systemic breakdown in a sort of social, um, contract that is the value of a university degree.

Speaker B: I agree with everything you say, but I'd also like to add that that sort of erosion or corrosion of trust is across all institutions. The legal system, the police, the church, the financial system. So in fact we're just part of the broader puzzle of that resettling the tectonic plates that make up the sort of the social contract that people have, but particularly in education. Everybody has an opinion about what a good education looks like. Everybody has an opinion about, employers have an opinion about, we want students to be able to do this. And now they're saying we don't need to employ so many People now because in fact we don't need, we can get AI to do some of those low level skills. So cochlear, uh, I have a friend that is the head of HR at cochlear. Once upon a time, every year they would recruit uh, 15 or so uh, HR people in Cochlear. Now it's down to five because they say, well, uh, AI can do some of that lower level stuff. So what I'm thinking is that we've actually got to rethink the purpose of university education and recalibrate what success looks like. Have you got any views on that?

Speaker C: Yeah, I uh, have been thinking a lot about this and it's.

Speaker B: My God, we're on the same page again.

Speaker C: I know. Well, because we're all at studiosity, we are both building the technologies and tools that are tailored to how we think we can best support, um, a trusted relationship in higher education between faculty and students. But we're also an employer of a growing number of people. And so we are employing recent graduates and we are seeing the types of people who are graduating. And because we are now coming up to four years since the release of large language models, we are now seeing the first graduates emerge who have effectively done their entire degree having free or, um, unconstrained access to foundation models to do work for them. And that means that this is the first cohort of new graduates who are, have been deeply impacted by this technology. And all of the research is showing that a lot of that impact has been a diminishment of their cognitive skills and capabilities because of an over reliance on technology that takes some of the cognitive work involved in learning and lets the machine do it. We've all heard this phrase cognitive offloading, um, when you let the machine do the thinking for you. But a lot of the research now is talking about cognitive surrender, um, and that is an even more serious worry where we see that um, people who've been using these tools for a long time don't have the capacity to start thinking for themselves the same way that they would have had they not been exposed to these tools. So yeah, there is a changing nature of the landscape of employment. But I would caution all of us to think twice about taking our advice about what a university degree should entail from the current needs of HR departments and large corporations. Because those will change in three minutes or three weeks or three months, or certainly you will in the time it takes a student to graduate. What won't change is the need for people to be able to think, especially when The WI fi is down, especially when they're out of range of 5G. Students who want to have a possibility of having a productive and functional employment, uh, trajectory, um, won't need to be that different from what they were pre generative AI. What they'll need to be able to do is to think for themselves, to have knowledge of a discipline and be able to put ideas together, synthesize ideas into new ideas, and also to have the ability to detect truth from fiction. And if you use AI too much and in the wrong ways, you don't develop those skills. And that is something that no employer will be able to tolerate. Right? So I don't think this is as complicated as we think it is. But when, when, when, when the public, when the media, when business says everyone needs to be AI literate and just sort of bandies about that phrase and doesn't define what it means, uh, I think that's really quite harmful because, um, you could also say that AI is a technology that is actually incredibly easy to learn to use once you're well educated, but it's also incredibly dangerous to use before you're well educated. So there's a real opportunity here for universities to double down on the teaching and learning experience. I feel like what's really happened here, Judith, to your anthropologist point, is that AI revealed that we just weren't, as a sector, investing as much as we needed to in teaching and learning for the last 15 or maybe even 20 years. And now we've realized, oh my goodness, we've kind of let the, uh, teaching and learning experience play second fiddle to the research and institutional objectives that are outside of that core part of what should be our mission. And now we're really a bit behind the eight ball, and we need to double down, triple down on a better teaching and learning experience to really show students, look, we're going to prepare you for the modern world. We're going to prepare you to be able to use this technology in really thoughtful, clever, important ways. So you are going to be employable, but you're also going to live a happy life. But trust us, we're going to give you the tools you need to use, and we're also going to tell you the tools you don't want to use. And some of these tools that you've been using, those are tools for people who are brain surgeons and professors and, um, incredibly capable senior software engineers. But they're not for students, not yet. Because if you use them, you could hurt yourself quite badly. Ken.

Speaker B: So, um, just to play back What I'm hearing, trust is coming up again. And that sort of. It's no longer trusting in policies, it's no longer trusting in processes, but it's also no longer trusting in what students, the foundations that students need to learn. The second point that you didn't say, but for me is as somebody that was educated in the social sciences and humanities, there are certain sorts of knowledges and information that are privileged. STEM is privileged over the humanities and social sciences. But we know that through social sciences and humanities you get to develop those critical thinking. You learn, you have to understand, you have to apply, and then you actually have to evaluate. You still have to do that in the STEM disciplines, but it's a different paradigm and a different framework in the social sciences. So, you know, we've seen throughout Australia, but also in American universities and UK universities, the removal of various disciplines, particularly sociology, philosophies surviving just. But a number of the sort of the critical disciplines that are asking the tough questions and asking for validation and really being able to justify and corroborate what you're saying are starting to be quietly moved out. Um, what's your view on the privileging of certain sorts of knowledges and uh, disciplines?

Speaker C: Well, this is the age old debate, right? Why should you go to university? Is it to learn how to think or to get a job? And in the modern age of 2026, the cost of higher education in Australia, in the US and Canada, in Great Britain, pretty much all around the English speaking world has gone up and up and up and people are more and more concerned about how much they're spending or how much debt they're incurring. So the job side of the equation has become more and more prevalent.

Speaker B: It's the measure of success.

Speaker C: It is the measure of success. And in fact, in Australia, for listeners who are not in Australia, we had a policy brought in by a previous Conservative government and maintained by the current labor government of, uh, what's called job ready graduates, in which we basically said to students, if you do a degree in a privileged subject like STEM or nursing or something like that, you can have your degree for a cut rate price. But if you want to do philosophy or you want to do literature, you want to do history, you're going to pay a lot more money. And by the way, those jobs don't pay that much anyway, so you might never pay your debt off. So we have policy settings in this country, for example, that are encouraging people to do some subjects than others. They don't really work and everyone knows that. But, um, there is so much anxiety about what comes after. Will there be a job for me, it's very hard for some people to imagine that um, a humanities related degree will be of value. And yet we're also seeing that what had been the most privileged of subjects, things like computer science, are at some of the most risk in terms of employment because it turns out that large language models are incredibly good at writing functional code. Not necessarily pretty code, not necessarily clean code, but code that is a good first draft. And um, that's something that historically was something that people needed to learn to do. And a lot of companies now aren't hiring junior devs because cloud code can make a senior developer effectively give them the capacity of having a few junior devs right at their side. So that's a worry that's shaken up the entire sector.

Speaker B: There's a whole new language that's also emerged over this time. And I just love the idea of um, the hallucination of the large language models. But when we realize that in fact if you read something and you don't have those critical skills, you don't know is this true or if this is not true. And I think social media is the perfect platform where you actually need critical skills to be able to sift the chaff from the grain. And there's a whole lot of chaff there and all these sorts of um, ill conceived and sort of um, populist remedies for health issues. Um, so why is it that we're not getting back to um, learning and teaching? Why isn't the focus on these critical skills and being able to demonstrate that you actually can um, perform at these levels.

Speaker C: Yeah, well you've asked a big question there. And this question of uh, hallucinations, this question of AI slop or of machines, um, making things up that sound incredibly plausible but turn out to be fiction is a huge problem. Right. Um, it's also a reflection of humans because these machines are trained on basically the entire Internet. And when you train a foundation model on the entire Internet, it means it's not just trained on largely factual content, let's say Wikipedia, but it's also trained on all sorts of stuff made up by people that's designed to sell ideas, to make people believe something they wouldn't otherwise have thought. Lots of social media posts and things like that. Um, I was talking to a computer scientist, the head of a computer science department at a large Australian research university little while ago and he estimated that at the launch of the first foundation models, the first public launch. Somewhere between 50 and 70% of all the content on the Internet was not fact based. It was either opinion or intentionally deceptive or fictional. Or fictional. And that probably has not changed. Or if anything, uh, it's gone up. Because now machines, these models, are able to produce more content trained on fiction. They produce more plausible fictions. Right. And it's fictions that will fool extremely experienced people. I was talking to a deputy vice chancellor at another university a few weeks ago and you know, she was presented with a list of references on a dis in her field, um, two papers in journals that looked and sounded completely plausible. And she believed them all until she started looking them up and realizing that many of them didn't exist. This is someone who's had a 35 year career in her field. This isn't even an adjacent field or an unrelated field, but right in her wheelhouse. And uh, she was fooled. So there's a, there's an enormous problem here. We're building machines that are capable of fooling the most experience and knowledge of us. Forget about students who really are at the beginning of their learning journeys and have almost no hope of being able to fact check this stuff without a lot of professional assistance from experts, academics.

Speaker B: So where does the accountability and responsibility lie? It's a matter of managing it and then providing ways forward.

Speaker C: Yeah, well, this has been the question we've been asking ourselves. What do we do now? Right. The machine is able to do so much work and yet we need to make sure that students are learning how to do that work. Because if they don't learn how to do that work, they'll never be able to evaluate or critique or manage the machine and ah, the machine will instead just manage them. So we came to the conclusion that it was this, the diminishment of trust in that teaching learning relationship which was really undermining the value of learning how to use the written word as an expression and a demonstration of human thinking. And we really wanted to help universities find a way to protect that. A lot of universities have sort of thrown up their hands and said, look, we can't use written assessments at all anymore because there's no way to validate them. Right. They fall outside the bounds of something that is, that is, um, that can be trusted. We just have to assume it's untrustworthy. We thought, well, if we could build some tools that would allow students to show what they know as opposed to universities using technologies to try and detect unethical or improper or um, inappropriate behavior, if we could, instead Give students tools so that they could show where and maybe how they've used technology, artificial intelligence, but also what they've been learning along the way and to demonstrate their learning. That would be a way. And we could give a picture of that learning to their instructor. Then their instructor could, instead of using, looking at this from a deficit model and saying well uh, let's just try and see, you know, what corners Judith cut instead being able to see, let's see what Judith has learned and maybe what Judith has learned from a first draft of something or the beginning of the semester to a somewhat more finished piece of work produced now. So we built this technology, um, we acquired this company called Norvalid and we've taken the kernel of the technology that they had were pioneering and we built it into our platform and it's called Validate. And the idea is that it allows a student to demonstrate what they know about the content and the work that they are submitting and to do this in an iterative process so that they can continue to demonstrate um, their learning journey and then to present that with a uh, validation to give approval, um, that reflects their own instructors guidelines in terms of how AI may or may not be used in a particular unit of study or on a particular task. And that we think is the right kind of way to use this technology to bring trust back into that relationship.

Speaker B: But it's also personalizing the experience. So it does have workload implications um, for the academic who's teaching it, who's teaching the course in terms of this is what I'm looking for. So you actually. The academic identifies the standards and thresholds but the academic also can provide feedback so that the student actually learns. It's a process of continuous learning and continuous improvement.

Speaker C: Yeah, that's exactly right. We're not trying to come up with a um, one time assessment. We're not trying to revert back to ah, a sort of pre digital age of um, in class only handwritten, maybe with a quill on a, you know, on a bit of vellum. Uh, we're not trying to turn the clock back. We're accepting this is what the technology looks like. Let's use it intelligently. Let's use it in a way that accelerates and augments the learning experience, speeds up the feedback circles and the feedback cycles so that students can get feedback quick, quickly and can improve more quickly. There's no doubt we can all learn more quickly when we can have some personalized learning experiences than when we're all sitting in a, you know, a factory type experience to getting two lectures a week and maybe, uh, impersonal discussion group of 30 or 40 people. We can do better than that and the technology should allow us to do that. But we don't need to use tools that are designed for trained professionals in the workplace, um, for students who are at the beginning of their learning journey. So the idea here is to say let's build much more educationally focused and appropriate tools, keep the faculty in control, let them set the parameters, and then have the students be given every opportunity to show what they know and what they've learned and how they've done what they've done. This isn't surveillance, it isn't detection. It isn't trying to catch anyone out, it's not trying to accuse anyone of bad behavior. No, it's about showing your learning journey. And we think that's going to be transformative because it's going to finally stop this totally toxic police and punish, um, obsession, which is just, that's a dead end for the sector if that continues.

Speaker B: Jack, earlier in our conversation you talked about the failure of governments to invest in learning and teaching. And we've got a lot of evidence about the difference in various institutions and the quality of the student experience and the quality of student learning outcomes. What advice would you give to government about breaking that silence in terms of the value of teaching and learning and investing in research, when in fact, good teaching provides the next generation of researchers? As I said last time we spoke.

Speaker C: Oh, it's a big chicken and egg problem, isn't it, Judith? Uh, you know, I don't know that any government can set a policy that can encourage any organization or individual to do the right thing or the wrong thing. We can certainly create incentives, and we have incentives that have built the systems that we have right now. Right. And, um, those, those incentives have been around. How do you get promoted as an academic? You be a good researcher, you get published, you get acknowledged by your peers, you get a good research ranking score, you do what you need to from a teaching perspective. But if you're a really good researcher, then the teaching side is quite, you know, diminished. Um, and what have, what have governments wanted? I mean, we have quite a sort of centralized control system in Australia, right. We have a small number of large universities and the government kind of tells them roughly what they should be doing and then they go off and do it and they all sort of converge on the same plan. And then 40 years later, the government throws up its hands and says, well, we have 40 universities that all look Very similar. How'd that happen? It happened because we set some rules in the 1990s that gave us the system we have today. Um, but it's, you know, so, so we don't have as diverse of a, of an ecosystem as obviously as the, the US or as even Canada and the UK have. But to a significant extent the global university ranking system has priority help has encouraged universities to prioritize certain, certain activities and investments over others. And the uh, teaching and learning side of their model, of their, their mission has been played second fiddle in for a lot of this time because it has not been a significant driver of rankings. And rankings are what drive um, reputation and they are also what drive recruitment of international students which is a huge part of the higher education economy. And in uh, APAC and also in um, in North America and especially in the UK as well. So how do we, how do we change that? I think it ultimately will have to come from university leadership to realize that, that this technology has been a, um, a sort of, you know, a dagger to the, the existing business model. And we in right to its heart and we really need to rethink and re reimagine how we are delivering education because there won't be a social license from the public to continue to do what we've done in the past if we don't figure out how to change what we're doing so that the vast majority of students who come to university wanting to learn, wanting to get skills and develop capabilities, actually do develop those skills and capabilities and end up being employable and participants in our emerging artificial intelligence driven economy. And we need to. I would hope that the sector right across the globe starts to think very, very quickly about how to go about putting the teaching and learning part of their mission on a par with, if not in front of the research side of the equation. Because uh, we don't need that many research only institutions and you know, they're all the top ranked ones anyway so they're going to be fine. But it's the vast, vast majority of institutions that you know, that I'm thinking about here. I think we're all concerned about finding a pathway forward.

Speaker B: As you know, I do work in a number of universities and um, it's interesting being a person that comes in and does consultancies and then leaves. But the consultancies are invariably practical. It seems to me that there are two observations I'd like to make. One of them is many universities are risk averse while they talk about differentiating themselves and that they should serve their communities. Invariably they mark off their success in comparison with universities that are fundamentally different. The university that I worked in, in Queensland I'm not going to name, had a particular history and it was unique and it should build on that. But instead a number of vice chancellors wanted it to look like UQ or qut. Its great strength was it was not UQ and it was not qut and it never would gain success to be like that. So that sense of it's lack of imagination, that's risk aversity and you know, being, being a bit timid about putting some really wild ideas forward. The second point is about governance. We've had a number of major crises in governance in Australian universities and we've seen it overseas as well. Once again, lack of trust. But it's the composition of the governing bodies for many people. They don't understand the business of universities. You bring in people whose benchmark is their own experience of the university 30 years ago. That's not going to take you forward. So that sense of a failure of governance and a risk aversity doesn't augur terribly well for reimagining the future of higher education.

Speaker C: Well, I mean, I've been thinking a lot about this as well Judith, and one analogy that sort of sits in my mind is the universities have been, you know, they've evolved over hundreds of years, maybe, maybe a thousand years. The business model of universities, the structure, the sort of semi autonomous, um, professors, professorships and faculties sort of hit in a loose, loosely sort of federated group of disciplines and then held together by an administration. Um, and that model, while it's evolved over those decades and centuries, is incredibly ill suited to the modern age. And by the modern age I mean the generative AI age that we are in right now. And it's almost like a, it's almost like a species that evolved on a, on an island continent, I know, like a, like a little marsupial in Australia. Um, and an invasive species arrives and it has no natural defenses. And that's part of the problem, right? That is a big part of the problem is that while universities are full of um, incredibly smart people researching most important pioneering subjects, their leadership is very much built on incremental change, caution and consensus driven decision making. And you just can't adapt when that is the model. So you're absolutely right. When governing bodies are full of people who've university experiences from them, you know, 30 or 40 years ago, that's a problem. It's, it's, it's the same Type of a problem when university leadership all had the same sorts of experiences. Because let's be honest, the university leadership and the governing bodies, they're similar ages, right? We all had very different undergraduate experiences than someone in their 20s or 30s even had. And that's part of, I think, what has been, um, you know, where the challenge lies. And this, it may be, this is the crisis that we have to have to um, for, for universities to finally realize what is at stake and to take some of the risks that you're suggesting they're going to need to take. But boy, it's very, very difficult to do that, um, when it's just not in your DNA to do that. And when, and when you've heard so many times that insert Name of technology MOOCs or you know, the Internet or DVDs or audio cassettes or videotape, um, was promised to be the real disruptor of higher education and they never really did it. But I think we are facing a different beast right now with generative AI and the challenges are, uh, orders of magnitude greater.

Speaker B: Jeff, there's a very interesting article that I read. I'm trying to bring generative AI and students together in the last part of our conversation. It's called AI is Robbing students of Intellectual Confidence. Um, this woman from Oxford Brookes University, um, said detection, uh, is unreliable, prohibition is unenforceable. And the underlying problem is not technological. The problem is that we have produced a generation who believe their own thinking is not good, good enough unless it sounds like a machine. That is a pedagogical failure, not a software one. Universities can address it, but only by valuing what AI cannot easily produce. Real time reasoning, productive uncertainty. The capacity to ask a bold question, to develop an idea rather than just present one. It means teaching students that hesitation is not failure, it's the start of thought. Do you want to respond to that?

Speaker C: Yeah. Well, you've heard this. You know, this is, this is the opposite of cognitive offloading or um, tunneling, letting the machine do all the thinking for you. We were trained. We were, we were prepped for this at least. Maybe not you and I, because we're not, we're not of this generation. But, but young people, people who were already deep into the social media landscape, right? I mean Instagram is a great example of this. Um, the idea of being able to, or post um, photos of oneself and the selfie, the emergence of the selfie with the smartphone, um, it rapidly became the case that nobody really wanted to post a photo that wasn't Doctored. That wasn't made to look perfect, that wasn't made to look beautiful. That wasn't made to look aspirational, to show what a wonderful life they had, when, in fact we all have kind of messy, grubby, kind of dirty lives. And our clothes don't look perfect and our hair is not right. Uh, but we filter everything. And it used to be that it was just filters that we chose, and if a photo didn't get enough, likes took it down. Right, because it wasn't perfect. What's happening with generative AI right now? Part of it is students don't want to look less than perfect. Part of it also is when you know that 50, 60, 70, 80. Now it's close to 95% of students, your peers, are all using it. And we know this from our student wellbeing surveys that we've been doing for the last decade around the globe. Judas, you know, everyone else is using it. The bar for achievement suddenly goes up if everyone else looks, you know, like, you know, Brad Pitt. I can't just go in, you know, looking like a, you know, everyday mug. That's terrible. So the pressure is astronomical to use these tools unless everyone puts down their weapons, right? And that's what these are. This is. This is a, uh, effectively a weapon. But we need everyone to put down the. Put down the weapons. And let's go back to learning how to learn. And what does that look like? What? You know, there's friction in learning. There is real effort needed in learning. There was an article in, uh, in the paper this morning, an interview with Terrence Tao, the mathematician, the brilliant mathematician from Adelaide who's been working in the US for decades. And he just got a big award over the weekend, and he was talking about just how important it is to just do the work yourself and to think, learn how to think and to solve problems on your own. He's a mathematician. He said, you know, my office at my university is just. Every wall is just blackboards. He said, I know there's machines that can do all this stuff now probably better than me, but I'm keeping the blackboards. I'm keeping them. I'm keeping my pen and paper because it takes me longer to do stuff, but I know that that's how I'm really understanding things, and that's how I figure things out. I thought it was so interesting. Here's a guy who's way better at math than probably half the planet combined, and he's very much focused on the messiness of the learning process. And I think we could all learn a lot by listening to someone like him and realizing that nobody looks perfect. Even Brad Pitt's probably pretty, you know, spotty. But um, you know, we just get used to the beautiful look of other people and we get fooled into thinking that is reality. It's not reality. It's really not. And that's, I think, what that article you're describing is pointing out. We need to encourage students to realize learning takes time. It's difficult, it's hard, but you'll know what it feels like and you'll feel better when you do it. And we're here to help you do it. We aren't here to catch you out doing the wrong thing. We want to give you the tools to do the right way. And if you do it with us, we can promise you that you will be AI literate. Because before you can be illiterate, you need to be literate. And that's maybe the piece we're forgetting. Let's make sure everyone is literate before they become AI literate. It doesn't work the other way around.

Speaker B: So Jack, when I did the other 70 odd podcasts, I uh, finished blousking the people I talk to. If we come back in three years time, what sort of things are we going to talk about? Let's make it one year's time. What do you think we'll talk about this time next year?

Speaker C: Well, I think there'll be two things that will be happening. One, the technology will continue to have improved dramatically and it will be able to do even more things than we thought. Will it get to the point of being self improving? Probably not quite yet, but there will be even more pressure on the sector. I think there will be some examples of some universities that have finally started to find their feet in dealing with teaching students how to be literate before they make them AI literate. But there will be more still that are still struggling to find their way. And I think that's just the nature of the organizational structures of universities. I worry that if it's taken three and a half years and we're still where we are, I don't think four and a half years is going to be that different. I think we are going to see some, there will be some, some existential crises for a growing number of universities. And that may be the thing that starts to change the decision making process in terms of how to respond with a reimagining of the curriculum and the teaching and learning experience. We're already seeing it with, with Universities, um, in the US and in the UK merging campuses, closing, um, relatively small numbers but the financial crisis and the pressure is enormous and it is building. So I think we will be, you know, we have this conversation in 12 months time. We will be, there will be some light on the horizon. Some maybe, maybe what we'd say is there will be several lights on the horizon. Some of them will be rising stars and some of them will be trains coming towards us. And the smart universities will be the ones that are figuring out which is which and getting out of the way of the trains and aiming their ships towards the stars.

Speaker B: I agree with you, but there are two things that concern me. One is the overregulation of higher education through external bodies, the development of standards that aren't necessarily tested but they're popular. So that's sort of the over regulation. And the second one is not having to uh, not, not having reinstated trust in a way that just makes people confident in what universities do and the value that universities have. Universities are big employers. Universities are places where young people come to grow up. Universities are places where people connect with diversity of ideas and diversity of peoples. And I think if we try this one size fits all, this lack of diversity, this lack of differentiating we will have, it will be problematic. But I don't want to be a Cassandra.

Speaker C: Yeah, actually you know, if I could wish for one thing for universities, especially in Australia to do it would be to not wait around for government policies to change, to tell them what to do next. They've got their, you know, as one DVC said to me, and he said it to lots of people actually, and he said it quite publicly, universities are full of incredibly smart people who know so many things. We just need to let them loose and they will do amazing stuff. I would love to see universities actually do that and to really take a, uh, you know, from the brass tacks approach and tackle what they think they should be doing without waiting for, you know, um, you know, a bureaucracy in the capital to tell them what they should be doing. To your last point about that sort of social license, I think if we saw some universities doing that and really being open to self critique and external critique, I think they would get their social license back in a heartbeat. I think that simply being able to look in the mirror and say, you know, we, we haven't been our best selves in the past recently, we can do better as opposed to being defensive about that but really owning it and saying the world has changed and we need to change what we're doing and let's put students and the teaching staff at the heart of what we're doing. I think that would go down a treat, uh, right across the globe. And any university that takes that approach and says, we're not going to wait for anyone to tell us how to do it, we think we can do it. We just need to reprioritize things and put the resources and we have plenty of them, we just need to redirect them. We can build the university of the future. And I think the opportunity is waiting there for some brave souls who want to do it. But, um, I think that the really brave thing would be to not do that, because I think if you don't do it, it's going to happen to you.

Speaker B: Jack, this has been a wonderful conversation as opposed to question and answer. And, um, I look forward to us coming together again this time next year just to catch up and ask a different set of questions and provides a different set of, uh, lenses in which to, um, deconstruct what's happening.

Speaker C: Well, thank you, Judith. And let's not leave it for three and a half more years. I agree. Let's do it. Let's do it in a year.

Speaker B: Thank you, Jake.

Speaker A: You have been listening to Studiosity's podcast, Reimagining Higher Education. Candid conversations within higher education. Sharing stories of leadership, change and best practices in teaching and learning. Visit studiosity.

Speaker C: Com.